Spatial Statistics Tools（空间统计工具）

空间统计工具

1、分析模式

# Process: 增量空间自相关
arcpy.IncrementalSpatialAutocorrelation_stats("", "", "10", "", "", "EUCLIDEAN", "ROW_STANDARDIZATION", 输出表, 输出报表文件)

# Process: 多距离空间聚类分析(Ripleys K 函数)
arcpy.MultiDistanceSpatialClustering_stats("", 输出表__2_, "10", "0_PERMUTATIONS_-_NO_CONFIDENCE_ENVELOPE", "NO_DISPLAY", "", "", "", "NONE", "MINIMUM_ENCLOSING_RECTANGLE", "")

# Process: 平均最近邻
arcpy.AverageNearestNeighbor_stats("", "EUCLIDEAN_DISTANCE", "NO_REPORT", "")

# Process: 空间自相关(Moran I)
arcpy.SpatialAutocorrelation_stats("", "", "NO_REPORT", "INVERSE_DISTANCE", "EUCLIDEAN_DISTANCE", "NONE", "", "")

# Process: 高/低聚类(Getis-Ord General G)
arcpy.HighLowClustering_stats("", "", "NO_REPORT", "INVERSE_DISTANCE", "EUCLIDEAN_DISTANCE", "NONE", "", "")

2、工具

# Process: 将空间权重矩阵转换为表
arcpy.ConvertSpatialWeightsMatrixtoTable_stats("", 输出表)

# Process: 将要素属性导出到 ASCII
arcpy.ExportXYv_stats("", "", "SPACE", 输出_ASCII_文件, "NO_FIELD_NAMES")

# Process: 收集事件
arcpy.CollectEvents_stats("", 输出加权点要素类)

# Process: 计算近邻点距离
arcpy.CalculateDistanceBand_stats("", "1", "EUCLIDEAN_DISTANCE")

3、度量地理分布

# Process: 中位数中心
arcpy.MedianCenter_stats("", 输出要素类, "", "", "")

# Process: 中心要素
arcpy.CentralFeature_stats("", 输出要素类__2_, "EUCLIDEAN_DISTANCE", "", "", "")

# Process: 平均中心
arcpy.MeanCenter_stats("", 输出要素类__3_, "", "", "")

# Process: 方向分布(标准差椭圆)
arcpy.DirectionalDistribution_stats("", 输出椭圆要素类, "1_STANDARD_DEVIATION", "", "")

# Process: 标准距离
arcpy.StandardDistance_stats("", 输出标准距离要素类, "1_STANDARD_DEVIATION", "", "")

# Process: 线性方向平均值
arcpy.DirectionalMean_stats("", 输出要素类__4_, "DIRECTION", "")

4、空间关系建模

# Process: 地理加权回归
arcpy.GeographicallyWeightedRegression_stats("", "", "", 输出要素类, "FIXED", "AICc", "", "30", "", "", "", "", "", 输出预测要素类)

# Process: 探索性回归
arcpy.ExploratoryRegression_stats("", "", "", "", 输出报表文件, 输出结果表, "5", "1", "0.5", "0.05", "7.5", "0.1", "0.1")

# Process: 普通最小二乘法
arcpy.OrdinaryLeastSquares_stats("", "", 输出要素类__2_, "", "", 系数输出表, 诊断输出表, 输出报表文件__2_)

# Process: 生成空间权重矩阵
arcpy.GenerateSpatialWeightsMatrix_stats("", "", 输出空间权重矩阵文件, "", "EUCLIDEAN", "1", "", "", "ROW_STANDARDIZATION", "", "", "", "")

# Process: 生成网络空间权重
arcpy.GenerateNetworkSpatialWeights_stats("", "", 输出空间权重矩阵文件__2_, "", "", "", "", "", "ALLOW_UTURNS", "", "NO_HIERARCHY", "5000 Meters", "INVERSE", "1", "ROW_STANDARDIZATION", "", "")

5、聚类分布制图

# Process: 优化的异常值分析
arcpy.OptimizedOutlierAnalysis_stats("", 输出要素, "", "COUNT_INCIDENTS_WITHIN_FISHNET_POLYGONS", "", "", "BALANCED_499", "", "")

# Process: 优化的热点分析
arcpy.OptimizedHotSpotAnalysis_stats("", 输出要素__2_, "", "COUNT_INCIDENTS_WITHIN_FISHNET_POLYGONS", "", "", 密度表面, "", "")

# Process: 分组分析
arcpy.GroupingAnalysis_stats("", "", 输出要素类, "2", "", "", "EUCLIDEAN", "", "", "FIND_SEED_LOCATIONS", "", 输出报表文件, "DO_NOT_EVALUATE")

# Process: 热点分析(Getis-Ord Gi*)
arcpy.HotSpots_stats("", "", 输出要素类__2_, "FIXED_DISTANCE_BAND", "EUCLIDEAN_DISTANCE", "NONE", "", "", "", "NO_FDR")

# Process: 相似搜索
arcpy.SimilaritySearch_stats("", "", 输出要素__3_, "NO_COLLAPSE", "MOST_SIMILAR", "ATTRIBUTE_VALUES", "10", "", "")

# Process: 聚类和异常值分析(Anselin Local Moran I)
arcpy.ClustersOutliers_stats("", "", 输出要素类__3_, "INVERSE_DISTANCE", "EUCLIDEAN_DISTANCE", "NONE", "", "", "NO_FDR", "499")